Attenuation prediction for fade mitigation using neural network with in situ learning algorithm

Details Attenuation prediction for fade mitigation using neural network with in situ learning algorithm Attenuation prediction for fade mitigation using neural network with in situ learning algorithm

Jan 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012adspr..49..336r&link_type=abstract

Advances in Space Research, Volume 49, Issue 2, p. 336-350.

Computer Science

Learning

Scientific paper

Increasing demand of bandwidth in communication satellites has forced satellite links to be designed in Ku bands and above. But at these frequencies, rain and other tropospheric elements result in large attenuation. To mitigate the tropospheric attenuation of microwave satellite signals above 10 GHz using any standard Fade Mitigation Technique (FMT), it is essential to have a priori knowledge about the level of attenuation. Hence, short-term rain attenuation prediction models play a key role in maintaining the link in which necessary compensation can be applied depending on the early information of attenuation. This paper presents a method of attenuation prediction using Adaptive Artificial Neural Network. Here In situ Learning Algorithm (ILA) has been used to enable the system to track the non-stationary nature of the attenuation. To validate this, Ku Band data, collected at three different sites in India have been used for the purpose of prediction. The performance of the algorithm is determined through the estimation of prediction accuracy by comparing the predicted values with the measured data. Results obtained using the mentioned technique shows considerably good accuracy even up to 20 s of prediction interval with acceptable ratio between the under and over predictions. The prediction performance is evaluated for different prediction intervals. Furthermore the present model is also compared with the persistence model and the relative performance is quantified.

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